Project Summary Familial hypercholesterolemia (FH) is a common genetic disorder, affecting every 200-1000 people, depending on the population and diagnostic criteria. FH leads to lifetime raised low-density lipoprotein (LDL) cholesterol, a high risk for premature atherosclerosis and downstream coronary heart disease. FH is designated as Tier 1 disease by the Center for Disease Control and Prevention, notably one of only three such diseases, because it is common, is associated with a high risk of premature illness, and is treatable with lifestyle or medications. Great uncertainty exists about the optimal approach to FH screening, which is reflected in conflicting recommendations in national screening guidelines. We propose to synthesize high quality data from national surveys and population-based cohort studies in a health policy computer simulation model comparing the health and economic value of different FH screening strategies. This study will prioritize the optimal approaches to FH screening in the U.S. population, identifying optimal initial screening age and defining the role of genetic testing in screening. We have assembled a team of experts in pediatric preventive cardiology, decision analysis, cardiovascular disease epidemiology, population genetics, biostatistics, health economic evaluation, and computer simulation modeling in order to evaluate and compare different FH screening strategies in children and adults. We aim to use this expertise and these methods in order to: ? Quantify diagnostic yield, clinical effectiveness, and economic value of universal FH phenotype screening in childhood or adulthood, and the added value of FH genotype screening ? Compare universal FH screening to the alternatives of using family history or a Big Data-based algorithm to direct targeted screening limited to children and adults with possible FH diagnosis ? Quantify the health and economic value of cascade screening families of FH cases We hypothesize that FH screening in childhood will be the highest value screening strategy in the U.S. population, and that genetic testing will improve diagnosis and treatment decisions most in cases of diagnostic uncertainty (e.g., borderline high cholesterol or absent family history). We hypothesize that a machine-learning algorithm will avoid the costs and complexity of universal screening, while yielding a similar case yield, as long as cholesterol testing is sufficiently common in children. This study will identify the optimal approach to FH screening in the U.S. population and the most influential data based on current knowledge and set the stage for efficiently designed clinical trials of FH screening. This study will be a test case for the concept of a ?precision? population health approach to screening for genetically-determined diseases in the general population.